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Learning In Graphical Models


Author : M.I. Jordan
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06



Download Learning In Graphical Models written by M.I. Jordan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.


In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.

Learning Probabilistic Graphical Models In R


Author : David Bellot
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-04-29



Download Learning Probabilistic Graphical Models In R written by David Bellot and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-29 with Computers categories.


Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book Predict and use a probabilistic graphical models (PGM) as an expert system Comprehend how your computer can learn Bayesian modeling to solve real-world problems Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package Who This Book Is For This book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting. What You Will Learn Understand the concepts of PGM and which type of PGM to use for which problem Tune the model's parameters and explore new models automatically Understand the basic principles of Bayesian models, from simple to advanced Transform the old linear regression model into a powerful probabilistic model Use standard industry models but with the power of PGM Understand the advanced models used throughout today's industry See how to compute posterior distribution with exact and approximate inference algorithms In Detail Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models. We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction. Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems. Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.

Graphical Models For Machine Learning And Digital Communication


Author : Brendan J. Frey
language : en
Publisher: MIT Press
Release Date : 1998



Download Graphical Models For Machine Learning And Digital Communication written by Brendan J. Frey and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.


Content Description. #Includes bibliographical references and index.

Graphical Models


Author : Michael Irwin Jordan
language : en
Publisher: MIT Press
Release Date : 2001



Download Graphical Models written by Michael Irwin Jordan and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Mathematics categories.


This book exemplifies the interplay between the general formal framework of graphicalmodels and the exploration of new algorithm and architectures. The selections range fromfoundational papers of historical importance to results at the cutting edge of research.

Advances In Probabilistic Graphical Models


Author : Peter Lucas
language : en
Publisher: Springer
Release Date : 2009-09-02



Download Advances In Probabilistic Graphical Models written by Peter Lucas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-02 with Mathematics categories.


This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

Large Scale Directed Graphical Models Learning


Author :
language : en
Publisher:
Release Date : 2016



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Directed graphical models are a powerful statistical method to compactly describe directional or causal relationships among the set of variables in large-scale data. However, a number of statistical and computational challenges arise that make learning directed graphical models often impossible for large-scale data. These issues include: (1) model identifiability; (2) computational guarantee; (3) sample size guarantee; and (4) combining interventional experiments with observational data. In this thesis, we focus on learning directed graphical models by addressing the above four issues. In Chapter 3, we discuss learning Poisson DAG models for modeling large-scale multivariate count data problems where each node is a Poisson random variable conditioning on its parents. We address the question of (1) model identifiability and learning algorithms with (2) computational complexity and (3) sample complexity. We prove that Poisson DAG models are fully identifiable from observational data using the notion of overdispersion, and present a polynomial-time algorithm that learns the Poisson DAG model under suitable regularity conditions. Chapter 4 focuses on learning a broader class of DAG models in large-scale settings. We address the issue of (1) model identifiability and learning algorithms with (2) computational complexity and (3) sample complexity. We introduce a new class of identifiable DAG models which include many interesting classes of distributions such as Poisson, Binomial, Geometric, Exponential, Gamma, and many more, and prove that this class of DAG models is fully identifiable using the idea of overdispersion. Furthermore, we develop statistically consistent and computationally tractable learning algorithms for the new class of identifiable DAG models in high-dimensional settings. Our algorithms exploits the sparsity of the graphs and overdispersion property. Chapter 5 concerns learning general DAG models using a combination of observational and interventional (or experimental) data. Prior work has focused on algorithms using Markov equivalence class (MEC) for the DAG and then using do-calculus rules based on interventions to learn the additional directions. However it has been shown that existing passive and active learning strategies that rely on accurate recovery of the MEC do not scale well to large-scale graphs because recovering MEC for DAG models are not successful large-scale graphs. Hence, we prove (1) model identifiability using the notion of the moralized graphs, and develop passive and active learning algorithms (4) combining interventional experiments with observational data. Lastly in Chapter 6, we concern learning directed cyclic graphical (DCG) models. We focus on (1) model identifiability for directed graphical models with feedback. We provide two new identifiability assumptions with respect to sparsity of a graph and the number of d-separation rules, and compare these new identifiability assumptions to the widely-held faithfulness and minimality assumptions. Furthermore we develop search algorithms for small-scale DCG models based on our new identifiability assumptions.

Probabilistic Graphical Models


Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009



Download Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.


A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.